Skill UI

This guide helps developers diagnose and fix common issues encountered when integrating the Speak API. It covers critical errors such as authentication failures, incorrect audio formats, rate limiting (429), and session expiration. The resource includes comprehensive HTTP error code reference tables, diagnostic commands (cURL, ffprobe), and resilient code patterns for implementing robust, production-ready error recovery mechanisms.

A comprehensive diagnostic bundle designed for developers integrating with the Speak API. It systematically collects critical information, including authentication status, audio format validation (checking codecs, sample rates, and file size using ffprobe), and network connectivity tests for both Speak and OpenAI endpoints. Use this tool when troubleshooting complex language learning integration issues or verifying API setup.

A comprehensive guide and setup for building a fast, reliable local development workflow for language learning applications. It includes setting up mock tutor responses for offline testing, generating audio test fixtures using TTS, and implementing a debug mode for detailed pronunciation scoring analysis. Ideal for engineers setting up a new Speak-based integration or refining existing test pipelines.

This skill provides systematic patterns and best practices for transforming slow database queries into high-performance operations. It covers techniques like proper indexing, query plan analysis (e.g., using EXPLAIN), schema design improvements, and resolving common issues like N+1 query problems. Use it to debug performance bottlenecks, reduce database load, and ensure scalable application backends.

Provides a structured workflow for defining agent-agnostic subagents with isolated context, covering purpose definition, metadata configuration, prompt writing, and common specialist patterns for verification, debugging, security auditing, and review.

A comprehensive guide for deep PostgreSQL diagnostics, designed for advanced troubleshooting when standard methods fail. Covers using pg_stat_statements for identifying slow queries, analyzing lock contention with pg_locks, detecting connection leaks, debugging RLS policy conflicts, and profiling Edge Function cold starts. Essential for performance regression investigation and escalating issues to support.

A comprehensive diagnostic guide for resolving common errors across the entire Supabase stack, including PostgREST, PostgreSQL, Authentication, and Storage. Use this tool when encountering specific error codes (e.g., PGRST301, 42501) or debugging complex API failures like RLS violations or session expiry. It provides step-by-step instructions for identifying the error layer and applying the correct fix.

This tool collects a comprehensive, redacted diagnostic bundle from a Supabase project. It systematically tests connectivity, authentication (Auth), Realtime capabilities, Storage buckets, and Row-Level Security (RLS) policies. The resulting archive is designed for sharing with support teams to accelerate troubleshooting of complex issues like connection failures or misconfigurations.

A comprehensive guide for structured incident response when a Supabase-backed application fails. This runbook guides users through triaging issues, verifying platform status, inspecting connection pools, debugging stuck queries using pg_stat_activity, and analyzing Edge Function logs. Essential for root cause analysis and escalating issues to support.

A disciplined debugging ritual that forces you to investigate errors, reproduce issues, and analyze patterns before proposing fixes, ensuring root causes are understood across multi-component systems and minimizing risky guesswork under pressure.

TensorBoard delivers a unified interface to track training metrics, debug network behavior, compare experiments, inspect model graphs, and profile performance for PyTorch or TensorFlow workflows.

This skill provides a systematic, multi-step approach to identify and resolve all failing unit tests within a codebase. It guides the user through smart error grouping (e.g., grouping by ImportError or AttributeError), prioritizing fixes from infrastructure to business logic, and ensuring thorough verification to prevent regressions. Ideal for use after major refactoring, debugging CI/CD failures, or when explicitly tasked with making the test suite pass.